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Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 14-061/III
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
We consider the dynamic factor model where the loading matrix, the dynamic factors and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates have lower quadratic loss compared to the standard maximum likelihood estimates. We investigate the methods in a Monte Carlo study where we document the finite sample properties. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.
Schlagwörter: 
Importance sampling
Kalman filtering
Likelihood-based analysis
Posterior modes
Rao-Blackwellization
Shrinkage
JEL: 
C32
C43
Dokumentart: 
Working Paper
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